Evolving a Real-World Vehicle Warning System. Nate Kohl, Kenneth Stanley, Risto Miikkulainen,
Michael Samples, and Rini Sherony. In Proceedings of the Genetic and Evolutionary Computation Conference 2006, pp.
1681–1688, July 2006.
http://www.sigevo.org/gecco-2006/
Many serious automobile accidents could be avoided if drivers were warned of impending crashes before they occur. Creating such warning systems by hand, however, is a difficult and time-consuming task. This paper describes three advances toward evolving neural networks with NEAT (NeuroEvolution of Augmenting Topologies) to warn about such crashes in real-world environments. First, NEAT was evaluated in a complex, dynamic simulation with other cars, where it outperformed three hand-coded strawman warning policies and generated warning levels comparable with those of an open-road warning system. Second, warning networks were trained using raw pixel data from a simulated camera. Surprisingly, NEAT was able to generate warning networks that performed similarly to those trained with higher-level input and still outperformed the baseline hand-coded warning policies. Third, the NEAT approach was evaluated in the real world using a robotic vehicle testbed. Despite noisy and ambiguous sensor data, NEAT successfully evolved warning networks using both laser rangefinders and visual sensors. The results in this paper set the stage for developing warning networks for real-world traffic, which may someday save lives in real vehicles.
@InProceedings{kohl:gecco06,
author = "Nate Kohl and Kenneth Stanley and Risto Miikkulainen and Michael Samples and Rini Sherony",
title = "Evolving a Real-World Vehicle Warning System",
booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference 2006",
year = "2006",
month = "July",
pages = "1681--1688",
abstract = {
Many serious automobile accidents could be avoided if drivers were
warned of impending crashes before they occur. Creating such
warning systems by hand, however, is a difficult and time-consuming
task. This paper describes three advances toward evolving neural
networks with NEAT (NeuroEvolution of Augmenting Topologies) to warn
about such crashes in real-world environments. First, NEAT was
evaluated in a complex, dynamic simulation with other cars, where it
outperformed three hand-coded strawman warning policies and
generated warning levels comparable with those of an open-road
warning system. Second, warning networks were trained using raw
pixel data from a simulated camera. Surprisingly, NEAT was able to
generate warning networks that performed similarly to those trained
with higher-level input and still outperformed the baseline
hand-coded warning policies. Third, the NEAT approach was evaluated
in the real world using a robotic vehicle testbed. Despite noisy
and ambiguous sensor data, NEAT successfully evolved warning
networks using both laser rangefinders and visual sensors. The
results in this paper set the stage for developing warning networks
for real-world traffic, which may someday save lives in real
vehicles.
},
wwwnote = {<a href="http://www.sigevo.org/gecco-2006/">http://www.sigevo.org/gecco-2006/</a>},
bib2html_pubtype = {Refereed Conference},
bib2html_rescat = {Machine Learning}
}
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